What is Predictive Analytics?
Predictive analytics is another branch of business analytics. Predictive analytics is defined as the process of focusing on predicting the possible outcome using machine-learning techniques like SVM, random forests and statistical models. It tries to forecast on the basis of previous data and scenarios.
So, this is used to find answers to questions like “What is likely to happen?”
For example, a hotel chain owner might ramp down promotional offers during a restive season of rains in a coastal area. This is based on the predictions that there are going to be fewer footfalls due to heavy rain. It merely is able to predict the probability that an event will occur. If predictive analysis model is tuned properly based on historical data, it can be used to support complex predictions in marketing and sales.
Table of Content
- 1 What is Predictive Analytics?
- 2 Importance of Predictive Analytics
- 3 Roles of Predictive Analytics in Different Fields
- 4 Using Predictive Analytics to Influence Business Outcomes
- 5 Tools Used for Predictive Analytics
- 6 Predictive Analytics vs Machine Learning
Predictive analytics mainly predicts the likelihood of events in future. In other words, it answers “What could happen?” with the help of statistical models and various techniques of forecasting. It predicts the near future probabilities and trends and helps in ‘what-if’ analysis. In predictive analytics, we use statistics, data mining techniques, and machine learning to analyse the future.
Importance of Predictive Analytics
Predictive analytics is being used by businesses to address issues and find new possibilities. The following are some examples of common applications:
Using a combination of analytics tools can help spot patterns and prevent illegal conduct. As the threat of fraud, zero-day vulnerabilities, and advanced persistent attacks grows, high-performance behavioural analytics analyses all network events in real-time to detect anomalies that might signal fraud, zero-day vulnerabilities and advanced persistent threats.
Optimising marketing campaigns
Predictive analytics is used to predict consumer responses and purchases, as well as cross-sell opportunities. Businesses may use predictive models to acquire, keep and expand their most profitable consumers.
Predictive models are used by many businesses to forecast inventory and manage resources. Predictive analytics is used by airlines to determine ticket prices. To optimise occupancy and income, hotels strive to estimate the number of guests for any particular night. Predictive analytics allows businesses to operate more effectively.
Credit scores, a well-known example of predictive analytics, are used to estimate a buyer’s chance of defaulting on a transaction. A credit score is a number calculated using a prediction model that takes into account all relevant information about a person’s creditworthiness. Insurance claims and collections are two more risk-related applications.
Roles of Predictive Analytics in Different Fields
The following describe the roles of predictive analytics in some fields as examples:
Let’s have a look at several internet merchants (e-trailers, if you will). As an example, I frequently use Amazon. It surprises me how effectively they employ predictive analytics. They always seem to know just what I want to get next. They frequently tease me with my next purchase by using the data they’ve previously gathered from my previous transaction. Spotify is in a similar boat. Don’t you just adore how they can give you a great recommendation for what to listen to next? Software developers that know how to apply predictive analytics generate this magic.
Predictive analytics is used by businesses to reduce risk. As an example, consider a financial organization. For example, I go to a bank and ask for a loan. However, in the past, I’ve been notorious for being late with payments or missing them exclusively. A bank can therefore determine whether or not I’m a good candidate for a loan based on my previous data, so reducing risk.
Predictive analytics is currently used in almost every industry. Healthcare, on the other hand, should not come as a surprise. Data is already being used in the healthcare business to improve patient care and censored incidentals. According to ArborMetrix, predictive analytics can analyse past patient data using AI and machine learning. The system can then predict disease risks for particular patients.
Using Predictive Analytics to Influence Business Outcomes
Predictive analytics provides you valuable perceptions about future outcomes. As it is a collection of statistical algorithms, data mining and machine learning to explore past data and forecast future outcomes. The use of predictive analytics was started in the 17th century.
Now, the advanced predictive analytics methods considered as a part ordinary business, empowering organizations to influence big data in terms of proactively determining risks and opportunities. Predictive analytics is more available than ever before in modern technology and the inclusive predictive analytics market estimated to extend to almost $28.1 billion by 2026.
Almost every organization can take advantage from predicting future performance implementing predictive analytics for measuring and manipulating risk. Some of the examples of predictive analytics to influence business outcomes are as follows:
It’s time to make sales when you’ve hired people and extended your business procedures. Predictive modelling analyses your data and predicts what you would expect to see using techniques like statistical regression. By evaluating prior sales trends performed at various points during your sales history, predictive analysis systems can anticipate the prospective sales you can generate.
If you have salespeople, you may also anticipate their individual sales efficiency by looking at their historic sales records. Targets that are likely to be met can easily be increased, while those that are probable to be missed allow us to act quickly to avoid high losses. Furthermore, after precisely projecting prospective future sales, the appropriate company strategy can be implemented.
It’s all about increasing earnings and returns in business. Your company or business unit may demand more complex tools and services to increase its market potential in today’s commercial environment, where online marketing and ecommerce have become so popular. These services would fall under the category of web analytics analysis.
Companies are increasingly relying on new algorithms to assist themselves or their BI consultant in locating qualified leads based on their potential customers’ ability to buy. As a result, these organisations are able to attract more qualified clients who are more interested in their offering. They do all this by identifying new and different ways to engage with people online in fresh and relevant ways in many circumstances.
Tools Used for Predictive Analytics
Tools in predictive analytics should be used for precise sales forecasts. It estimates the future outcomes based on the current and past data, to predict what will happen in the future due to which you can understand the requirement and need of your customers, thus leading to better consumer’s experiences.
There are several predictive analytics tools in the market in which to choose the right one sometimes appears as intimidating. These tools are not used for a single purpose but they can be used across many factors comprising functions, price and no. of users etc.
Some of the most popular tools used in predictive analytics are as follows:
SAP analytics cloud
It is mainly focused on BI, predictive analytics and planning. A large no. of dataset such as predictive forecasting, economic forecasting, data consideration and conception all are possible through this app.
IBM Watson studio
it is a predictive analysis platform which helps users with statistical analysis, a huge library of machine learning algorithms, integration with big data and open basis extensibility into the applications.
It is a self-service platform that delivers several kinds of products for the business needs. Alteryx also has a spontaneous drag-and-drop UI, which makes it enormously user-friendly.
Microsoft Azure Machine Learning
Azure Machine Learning (Azure ML) is a cloud-based service that allows users to create and manage machine learning solutions. It’s intended to assist data analysts and machine learning engineers in maximising the use of their present data handling and model creation abilities and frameworks. Assist them in scaling, distributing, and deploying their workloads to the cloud as well.
Statistical analysis software (SAS)
SAS is a statistical data analysis programme. SAS’s primary function is to retrieve, report, and analyse statistical data. In the SAS environment, each statement must conclude with a semicolon; otherwise, the statement will generate an error notice. It’s a strong tool for performing SQL queries and using macros to automate user tasks.
Apart from that, SAS provides descriptive visualisation through graphs, and other SAS versions include machine learning, data mining, time series, and other types of reporting. SAS allows for two different sorts of statements to be used to run the programme. The statements of an SAS programme are divided into two categories: data steps and procedures.
H2O Driverless AI
It resolves complicated business problems and enhances the foundation of new ideas with outcomes you can comprehend and belief.
Predictive Analytics vs Machine Learning
|Predictive Analytics||Machine Learning|
|Predictive analytics is a statistical method of analysing data.||Machine learning is a type of computing.|
|A machine-learning algorithm is frequently used in predictive analytics.||Predictive analytics are not always the result of machine learning.|
|In comparison to machine learning, it does not need a great deal of code to complete a task.||In comparison to predictive analytics, it takes a lot of coding and a lot of data to complete a task.|
|The completion of a job necessitates human interaction.||Without human assistance, the machine is responsible for making decisions and completing tasks.|
|Its foundation is statistics, which is crucial in it.||It is based on computer science.|